Read in Script
source("Safegraph_Script.R")
NAs introduced by coercion
rm(list=ls()[! ls() %in% c("rural_ipums","ruca", "subset_glass",
"poverty_ipums", "pct_chg_aug",
"industry_ipums", "aug_combined",
"buffer1_vec","buffer2_perim_vec",
"buffer2_vec", "cf_bf1", "cf_bf2", "gfperimvec",
"not_zcta", "rural_ruca",
'%notin%', 'pp1', 'pp2', 'aug_combined', 'com')])
Read in Spatial Plots
plot.new()
rasterImage(pp1,0,0,1,1)

plot.new()
rasterImage(pp2,0,0,1,1)

Initially a single 20-acre brush fire, it rapidly grew and merged with two smaller fires that expanded to 11,000 acres during the night of September 27 into September 28.
The Glass Fire was fully contained on October 20, 2020, after burning over 67,484 acres and destroying 1,555 structures, including 308 homes and 343 commercial buildings in Napa County, as well as 334 homes in Sonoma County
Data Tables (Descriptives)
Using shape files from the GlassFire, 10 km buffers were used to create the first buffer (BF1), 10km surrounding BF1 creates the second buffer (BF2).
Each perimeter has unique ZCTA that is exclusive to their perimeter.
DT::datatable(com %>%
filter(june != 'June') %>%
mutate(new_date = as.POSIXct(strptime(date_range_start, "%Y-%m-%d"))) %>%
mutate(week = as_date(new_date)) %>%
group_by(city, zcta, week, fire_cat,
Poverty_Dummy_75, Agr_Dummy_75,
Rural_Dummy_R) %>%
summarize(sum_visit = sum(raw_visit_counts), .groups = 'drop'),
caption = 'Descriptive Statistics of August Mobility',
filter = "top",
extensions = c("SearchPanes", "Select", "Buttons"),
options = list(dom = "Btip",buttons = list("searchPanes")))
DT::datatable(com %>%
filter(june == 'June') %>%
mutate(new_date = as.POSIXct(strptime(date_range_start, "%Y-%m-%d"))) %>%
mutate(week = as_date(new_date)) %>%
group_by(city, zcta, week, fire_cat,
Poverty_Dummy_75, Agr_Dummy_75,
Rural_Dummy_R) %>%
summarize(sum_visit = sum(raw_visit_counts), .groups = 'drop'),
caption = 'Descriptive Statistics of June Mobility',
filter = "top",
extensions = c("SearchPanes", "Select", "Buttons"),
options = list(dom = "Btip",buttons = list("searchPanes")))
Visualizations
Percent Change Using Fire Category and June as Baseline
pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta,fire_cat) %>%
summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
ggplot(data =., aes(x = week, y = med, group = zcta)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=fire_cat), alpha = 0.2) +
geom_point(aes(colour = fire_cat), alpha = 0.8) +
ylim(-50,200) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) +
labs(x="Date", y="Median Percent Change (Divide by 100)") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black")) +
annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"),
ymin = -50.00, ymax = 150.00 ,
alpha = 0, color= "orange") +
ggtitle("Percent Change Mobility by Fire Category")

When looking at mobility: + Week of June 6, 2020 to the week of June 22, 2020 is used as a baseline + Percent change is calculated by week to week in each consecutive week in June + The median of percent change is then grouped by fire category in June and is then officially the baseline for August mobility + Then the percent change of August is then used for each consecutive week in August (August 31 - October 12) + A calculation of percent change is taken by using the weekly percentage change for August in relation to the fire category median percent change.
| GF |
0.02 |
| BF1 |
0.03 |
| BF2 |
0.04 |
Percent Change of Mobility by Rural/ Fire Category and June as Baseline
pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta,Rural_Dummy_R, fire_cat) %>%
summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
ggplot(data =., aes(x = week, y = med, group = zcta)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Rural_Dummy_R), alpha = 0.2) +
geom_point(aes(colour = Rural_Dummy_R), alpha = 0.8) +
ylim(-50,200) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) +
labs(x="Date", y="Median Percent Change (Divide by 100") +
facet_wrap(vars(fire_cat), nrow = 3) +
ggtitle("Percent Change Mobility by Fire Category and Rural")

pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta, fire_cat) %>%
group_by(Rural_Dummy_R, week, fire_cat) %>%
summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
ggplot(data =., aes(x = week, y = med, group = Rural_Dummy_R)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Rural_Dummy_R)) +
geom_point(size = 2) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) +ylim(-50,50) +
ggtitle("Percent Change Mobility by Fire Category and Rural (Collapsed)") +
labs(x="Date", y="Median Percent Change (Divide by 100")

pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta) %>%
group_by(Rural_Dummy_R, week, fire_cat) %>%
filter(fire_cat == 'GF') %>%
summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
ggplot(data =., aes(x = week, y = med, group = Rural_Dummy_R)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Rural_Dummy_R)) +
geom_point(size = 2) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) +
labs(x="Date", y="Median Percent Change (Divide by 100)") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black")) +
annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"),
ymin = -20, ymax = 60 ,
alpha = 0, color= "orange") +
ggtitle("Median Raw Visits by Date (GF/Collapsed)")

Percent Change of Mobility by Agriculture/ Fire Category and June as Baseline
pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta,Agr_Dummy_75, fire_cat) %>%
summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
ggplot(data =., aes(x = week, y = med, group = zcta)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Agr_Dummy_75), alpha = 0.2) +
geom_point(aes(colour = Agr_Dummy_75), alpha = 0.8) +
ylim(-50,200) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) +
labs(x="Date", y="Median Percent Change") + facet_wrap(vars(fire_cat), nrow = 3)

# Agriculture BY Fire Cat
pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta, fire_cat) %>%
group_by(Agr_Dummy_75, week, fire_cat) %>%
summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
ggplot(data =., aes(x = week, y = med, group = Agr_Dummy_75)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Agr_Dummy_75)) +
geom_point(size = 2) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) +ylim(-50,50)
pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta) %>%
group_by(Agr_Dummy_75, week, fire_cat) %>%
filter(fire_cat == 'GF') %>%
summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
ggplot(data =., aes(x = week, y = med, group = Agr_Dummy_75)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Agr_Dummy_75)) +
geom_point(size = 2) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) +
labs(x="Date", y="Median Percent Change") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black")) +
annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"),
ymin = -20, ymax = 60 ,
alpha = 0, color= "orange")
ggtitle("Median Raw Visits by Date")
Percent Change of Mobility by Poverty/ Fire Category and June as Baseline
pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta,Poverty_Dummy_75, fire_cat) %>%
summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
ggplot(data =., aes(x = week, y = med, group = zcta)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Poverty_Dummy_75), alpha = 0.2) +
geom_point(aes(colour = Poverty_Dummy_75), alpha = 0.8) +
ylim(-50,200) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) +
labs(x="Date", y="Median Percent Change") + facet_wrap(vars(fire_cat), nrow = 3)

# Poverty BY Fire Cat
pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta, fire_cat) %>%
group_by(Poverty_Dummy_75, week, fire_cat) %>%
summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
ggplot(data =., aes(x = week, y = med, group = Poverty_Dummy_75)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Poverty_Dummy_75)) +
geom_point(size = 2) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) + ylim(-75,100)

pct_chg_aug %>%
na.omit() %>%
group_by(week,zcta) %>%
group_by(Poverty_Dummy_75, week, fire_cat) %>%
filter(fire_cat == 'GF') %>%
summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
ggplot(data =., aes(x = week, y = med, group = Poverty_Dummy_75)) +
scale_color_manual(values=c('Blue','Green', 'Red')) +
geom_line(aes(colour=Poverty_Dummy_75)) +
geom_point(size = 2) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
theme(axis.text.x = element_text(angle = 0)) +
labs(x="Date", y="Median Percent Change") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black")) +
annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"),
ymin = -25, ymax = 60 ,
alpha = 0, color= "orange")

ggtitle("Median Raw Visits by Date")
$title
[1] "Median Raw Visits by Date"
attr(,"class")
[1] "labels"
Looking at PM25 Levels
Grouped by Rural/Urban
pct_chg_aug %>%
na.omit() %>%
inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Rural_Dummy_R) %>%
group_by(week,zcta) %>%
arrange(zcta) %>%
ggplot(data = ., aes(x = week, y = PM25,
color = Rural_Dummy_R)) +
geom_point(aes(color = Rural_Dummy_R), alpha = .5) +
scale_color_manual(values=c('Blue','Green')) +
geom_smooth(se = F) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
labs(title="PM25 Values by Date and Category") +
labs(x="Date", y="PM25 Values") +
labs(color='Nation') +
annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"),
ymin = 0, ymax = 100 ,
alpha = 0, color= "orange") + facet_wrap(vars(fire_cat), nrow = 3)

Grouped by Agricultural
pct_chg_aug %>%
na.omit() %>%
inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Agr_Dummy_75) %>%
group_by(week,zcta) %>%
arrange(zcta) %>%
ggplot(data = ., aes(x = week, y = PM25,
color = Agr_Dummy_75)) +
geom_point(aes(color = Agr_Dummy_75), alpha = .5) +
scale_color_manual(values=c('Blue','Green')) +
geom_smooth(se = F) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
labs(title="PM25 Values by Date and Category") +
labs(x="Date", y="PM25 Values") +
labs(color='Nation') +
annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"),
ymin = 0, ymax = 100 ,
alpha = 0, color= "orange") + facet_wrap(vars(fire_cat), nrow = 3)

Grouped by Poverty
pct_chg_aug %>%
na.omit() %>%
inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Poverty_Dummy_75) %>%
group_by(week,zcta) %>%
arrange(zcta) %>%
ggplot(data = ., aes(x = week, y = PM25,
color = Poverty_Dummy_75)) +
geom_point(aes(color = Poverty_Dummy_75), alpha = .5) +
scale_color_manual(values=c('Blue','Green')) +
geom_smooth(se = F) +
scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
labs(title="PM25 Values by Date and Category") +
labs(x="Date", y="PM25 Values") +
labs(color='Nation') +
annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"),
ymin = 0, ymax = 100 ,
alpha = 0, color= "orange") + facet_wrap(vars(fire_cat), nrow = 3)

---
title: "SafeGraph Mobility EDA Markdown"
author: "Andrew Lai"
date: "30/04/2022"
output:   
  html_document:
    toc: yes
    df_print: paged
  html_notebook:
    toc: yes
    theme: spacelab
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Read in Script
```{r, message = FALSE}
source("Safegraph_Script.R")
```

```{r}
rm(list=ls()[! ls() %in% c("rural_ipums","ruca", "subset_glass", 
                           "poverty_ipums", "pct_chg_aug", 
                           "industry_ipums", "aug_combined", 
                           "buffer1_vec","buffer2_perim_vec", 
                           "buffer2_vec", "cf_bf1", "cf_bf2", "gfperimvec",
                           "not_zcta", "rural_ruca", 
                           '%notin%', 'pp1', 'pp2', 'aug_combined', 'com')])
```

## Read in Spatial Plots
```{r}
plot.new() 
rasterImage(pp1,0,0,1,1)
```


```{r}
plot.new() 
rasterImage(pp2,0,0,1,1)
```
Initially a single 20-acre brush fire, it rapidly grew and merged with two smaller fires that expanded to 11,000 acres during the night of September 27 into September 28.

The Glass Fire was fully contained on October 20, 2020, after burning over 67,484 acres and destroying 1,555 structures, including 308 homes and 343 commercial buildings in Napa County, as well as 334 homes in Sonoma County



## Data Tables (Descriptives)


Fire Category | Total ZCTA
------------- | -------------
GF            | 8
BF1           | 17
BF2           | 10


Using shape files from the GlassFire, 10 km buffers were used to create the first buffer (BF1), 10km surrounding BF1 creates the second buffer (BF2).

Each perimeter has unique ZCTA that is exclusive to their perimeter.  


```{r}
DT::datatable(com %>%
                filter(june != 'June') %>%
                mutate(new_date = as.POSIXct(strptime(date_range_start, "%Y-%m-%d"))) %>%
                mutate(week = as_date(new_date)) %>%
                group_by(city, zcta, week, fire_cat, 
                         Poverty_Dummy_75, Agr_Dummy_75,
                         Rural_Dummy_R) %>%
                summarize(sum_visit = sum(raw_visit_counts), .groups = 'drop'), 
              caption = 'Descriptive Statistics of August Mobility', 
              filter = "top",
              extensions = c("SearchPanes", "Select", "Buttons"),
              options = list(dom = "Btip",buttons = list("searchPanes")))
```


```{r}
DT::datatable(com %>%
                filter(june == 'June') %>%
                mutate(new_date = as.POSIXct(strptime(date_range_start, "%Y-%m-%d"))) %>%
                mutate(week = as_date(new_date)) %>%
                group_by(city, zcta, week, fire_cat, 
                         Poverty_Dummy_75, Agr_Dummy_75,
                         Rural_Dummy_R)  %>%
              summarize(sum_visit = sum(raw_visit_counts), .groups = 'drop'), 
              caption = 'Descriptive Statistics of June Mobility', 
              filter = "top",
              extensions = c("SearchPanes", "Select", "Buttons"),
              options = list(dom = "Btip",buttons = list("searchPanes")))
```

## Visualizations


### Percent Change Using Fire Category and June as Baseline


```{r}
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta,fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
  ggplot(data =., aes(x = week, y = med, group = zcta)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=fire_cat), alpha = 0.2) +
  geom_point(aes(colour = fire_cat), alpha = 0.8) +
  ylim(-50,200) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change (Divide by 100)") +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))  +
    annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = -50.00, ymax = 150.00 ,
             alpha = 0, color= "orange") +
  ggtitle("Percent Change Mobility by Fire Category")
```

When looking at mobility:
  + Week of June 6, 2020 to the week of June 22, 2020 is used as a baseline
  + Percent change is calculated by week to week in each consecutive week in June 
  + The median of percent change is then grouped by fire category in June and is then officially the
  baseline for August mobility
  + Then the percent change of August is then used for each consecutive week in August (August 31 - October 12)
  + A calculation of percent change is taken by using the weekly percentage change for August in relation to the fire category median percent change.  



Fire Category | Median Percent Change
------------- | -------------
GF            | 0.02
BF1           | 0.03
BF2           | 0.04



### Percent Change of Mobility by Rural/ Fire Category and June as Baseline

Rural Category | Total ZCTA
------------- | -------------
Rural         | 11
Urban         | 24

```{r}
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta,Rural_Dummy_R, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
  ggplot(data =., aes(x = week, y = med, group = zcta)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Rural_Dummy_R), alpha = 0.2) +
  geom_point(aes(colour = Rural_Dummy_R), alpha = 0.8) +
  ylim(-50,200) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change (Divide by 100") + 
  facet_wrap(vars(fire_cat), nrow = 3) +
  ggtitle("Percent Change Mobility by Fire Category and Rural")
```


```{r}
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta, fire_cat) %>%
  group_by(Rural_Dummy_R, week, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Rural_Dummy_R)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Rural_Dummy_R)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12")))  +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) +ylim(-50,50) +
  ggtitle("Percent Change Mobility by Fire Category and Rural (Collapsed)") +
  labs(x="Date", y="Median Percent Change (Divide by 100") 
```

```{r}
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta) %>%
  group_by(Rural_Dummy_R, week, fire_cat) %>%
  filter(fire_cat == 'GF') %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Rural_Dummy_R)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Rural_Dummy_R)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change (Divide by 100)") +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))  +
    annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = -20, ymax = 60 ,
             alpha = 0, color= "orange") +
  ggtitle("Median Raw Visits by Date (GF/Collapsed)")
```



### Percent Change of Mobility by Agriculture/ Fire Category and June as Baseline


Agr Category   | Total ZCTA
-------------  | -------------
Agr<75         | 26
Agr>75         | 9

```{r}
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta,Agr_Dummy_75, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
  ggplot(data =., aes(x = week, y = med, group = zcta)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Agr_Dummy_75), alpha = 0.2) +
  geom_point(aes(colour = Agr_Dummy_75), alpha = 0.8) +
  ylim(-50,200) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change") + facet_wrap(vars(fire_cat), nrow = 3)
```

```{r}
# Agriculture BY Fire Cat
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta, fire_cat) %>%
  group_by(Agr_Dummy_75, week, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Agr_Dummy_75)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Agr_Dummy_75)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12")))  +# week of Oct 12

  theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) +ylim(-50,50)
```



```{r}
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta) %>%
  group_by(Agr_Dummy_75, week, fire_cat) %>%
  filter(fire_cat == 'GF') %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Agr_Dummy_75)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Agr_Dummy_75)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change") +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))  +
    annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = -20, ymax = 60 ,
             alpha = 0, color= "orange")
  ggtitle("Median Raw Visits by Date") 
```



### Percent Change of Mobility by Poverty/ Fire Category and June as Baseline


Pov Category   | Total ZCTA
-------------  | -------------
Pov<75         | 26
Pov>75         | 9




```{r}
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta,Poverty_Dummy_75, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
  ggplot(data =., aes(x = week, y = med, group = zcta)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Poverty_Dummy_75), alpha = 0.2) +
  geom_point(aes(colour = Poverty_Dummy_75), alpha = 0.8) +
  ylim(-50,200) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change") + facet_wrap(vars(fire_cat), nrow = 3)
```

```{r}
# Poverty BY Fire Cat
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta, fire_cat) %>%
  group_by(Poverty_Dummy_75, week, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Poverty_Dummy_75)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Poverty_Dummy_75)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12")))  +# week of Oct 12

  theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) + ylim(-75,100)
```


```{r}
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta) %>%
  group_by(Poverty_Dummy_75, week, fire_cat) %>%
  filter(fire_cat == 'GF') %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Poverty_Dummy_75)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Poverty_Dummy_75)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change") +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))  +
    annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = -25, ymax = 60 ,
             alpha = 0, color= "orange")
  ggtitle("Median Raw Visits by Date")
```

### Looking at PM25 Levels 

#### Grouped by Rural/Urban
```{r}
pct_chg_aug %>%
  na.omit() %>%
  inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
  select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Rural_Dummy_R) %>%
  group_by(week,zcta) %>%
  arrange(zcta) %>%
  ggplot(data = ., aes(x = week, y = PM25, 
                               color = Rural_Dummy_R)) +
  geom_point(aes(color = Rural_Dummy_R), alpha = .5) +
  scale_color_manual(values=c('Blue','Green')) +
  geom_smooth(se = F) + 
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
  labs(title="PM25 Values by Date and Category") +
  labs(x="Date", y="PM25 Values") +
  labs(color='Nation')  +
  annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = 0, ymax = 100 ,
             alpha = 0, color= "orange")  + facet_wrap(vars(fire_cat), nrow = 3)
```

#### Grouped by Agricultural
```{r}
pct_chg_aug %>%
  na.omit() %>%
  inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
  select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Agr_Dummy_75) %>%
  group_by(week,zcta) %>%
  arrange(zcta) %>%
  ggplot(data = ., aes(x = week, y = PM25, 
                               color = Agr_Dummy_75)) +
  geom_point(aes(color = Agr_Dummy_75), alpha = .5) +
  scale_color_manual(values=c('Blue','Green')) +
  geom_smooth(se = F) + 
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
  labs(title="PM25 Values by Date and Category") +
  labs(x="Date", y="PM25 Values") +
  labs(color='Nation')  +
  annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = 0, ymax = 100 ,
             alpha = 0, color= "orange")  + facet_wrap(vars(fire_cat), nrow = 3)
```


#### Grouped by Poverty
```{r}
pct_chg_aug %>%
  na.omit() %>%
  inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
  select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Poverty_Dummy_75) %>%
  group_by(week,zcta) %>%
  arrange(zcta) %>%
  ggplot(data = ., aes(x = week, y = PM25, 
                               color = Poverty_Dummy_75)) +
  geom_point(aes(color = Poverty_Dummy_75), alpha = .5) +
  scale_color_manual(values=c('Blue','Green')) +
  geom_smooth(se = F) + 
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
  labs(title="PM25 Values by Date and Category") +
  labs(x="Date", y="PM25 Values") +
  labs(color='Nation')  +
  annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = 0, ymax = 100 ,
             alpha = 0, color= "orange")  + facet_wrap(vars(fire_cat), nrow = 3)
```

